Touchless testing platform

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a touchless testing platform employed to, for example, create automated testing scripts, sequence test cases, and implement determine defect solutions. In one aspect, a method includes the actions of receiving a log file that includes log records generated from a code base; processing the log file through a pattern mining algorithm to determine a usage pattern; generating a graphical representation based on an analysis of the usage pattern; processing the graphical representation through a machine learning algorithm to select a set of test cases from a plurality of test cases for the code base and to assign a priority value to each of the selected test cases; sequencing the set of test cases based on the priority values; and transmitting the sequenced set of test cases to a test execution engine.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/126,570, filed on Sep. 10, 2018, entitled “Touchless TestingPlatform,” which is a continuation of U.S. patent application Ser. No.15/911,968, filed on Mar. 5, 2018, entitled “Touchless TestingPlatform,” now U.S. Pat. No. 10,073,763, which claims priority to IndianPatent Application No. 201711046829, filed on Dec. 27, 2017, entitled“Touchless Testing Platform,” the entirety of which are herebyincorporated by reference in their entirety.

TECHNICAL FIELD

This application generally relates to generating and executing automatedtesting scripts.

BACKGROUND

Software applications are designed to accommodate a multitude oftransactions, where each transaction often requires the performance of asignificant number of functions. Testing of software applications mayinclude creating test cases based on requirements and then executing thetest cases through, for example, a test script to detect defects. Testcases may be automated using commercial and open source tools to reduceexecution time. For example, a regression test suite is a set of testcases, often written in the form of a script, designed to ensure thateach of these functions remain accurate and correct followingmodification of the underlying programing code. Execution of thesesuites helps to ensure that defects have not been introduced oruncovered in unchanged areas of the codebase as a result of themodification. Moreover, each level of testing (e.g., unit testing,system testing, and acceptance testing) may have its own regression testsuite. Providers of these software applications and/or theircorresponding services are faced with the problem of having largeregression test suites that are executed manually and the need toautomate these test suites to function within any one of a number ofindustry standard automation tools. Once automated, these test cases canbe executed repeatedly and frequently, which adds to the amount oftesting coverage for the respective application. However, effective andefficient management of such suites of test cases is both time consumingand effort intensive.

SUMMARY

Implementations of the present disclosure are generally directed to atouchless automated platform system. More specifically, implementationsare directed to a system that creates automated testing scripts based ontest cases determined according to requirements documentation, sequencesthe test cases for execution, and implements defect solutions determinedaccording to the specific application functionalities involved.

In a general implementation, a system includes one or more processors;and a computer-readable storage device coupled to the one or moreprocessors and having instructions stored thereon which, when executedby the one or more processors, cause the one or more processors toperform operations comprising: receiving a log file that includes logrecords generated from a code base; processing the log file through apattern-mining algorithm to determine a usage pattern; generating agraphical representation based on an analysis of the usage pattern;processing the graphical representation through a machine-learningalgorithm to select a set of test cases from a plurality of test casesfor the code base and to assign a priority value to each of the selectedtest cases; sequencing the set of test cases based on the priorityvalues; and transmitting the sequenced set of test cases to a testexecution engine.

In another general implementation, a computer-implemented methodexecuted by one or more processors includes: receiving a log file thatincludes log records generated from a code base; processing the log filethrough a pattern-mining algorithm to determine a usage pattern;generating a graphical representation based on an analysis of the usagepattern; processing the graphical representation through amachine-learning algorithm to select a set of test cases from aplurality of test cases for the code base and to assign a priority valueto each of the selected test cases; sequencing the set of test casesbased on the priority values; and transmitting the sequenced set of testcases to a test execution engine.

In yet another general implementation, one or more non-transitorycomputer-readable storage media coupled to one or more processors andhaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform operationsthat include: receiving a log file that includes log records generatedfrom a code base; processing the log file through a pattern-miningalgorithm to determine a usage pattern; generating a graphicalrepresentation based on an analysis of the usage pattern; processing thegraphical representation through a machine-learning algorithm to selecta set of test cases from a plurality of test cases for the code base andto assign a priority value to each of the selected test cases;sequencing the set of test cases based on the priority values; andtransmitting the sequenced set of test cases to a test execution engine.

In an aspect combinable with any of the general implementations, theoperations further include generating a report based on the usagepattern, the failure pattern, the business flow graph, or the anomaly;and transmitting the report to a control center application.

In another aspect combinable with any of the previous aspects, theoperations further include analyzing the usage pattern to identifypattern groups; and generating metadata for the identified patterngroups.

In another aspect combinable with any of the previous aspects, theoperations further include determining churn data based on an analysisof configuration and coding changes to the code base; processing testresults, the churn data, and the code base through an ArtificialIntelligence (AI) model, the AI model trained using training datacomprising resolutions to reported defects for the code base; anddetermining a likelihood of failure for at least one of functionalitiesof the code base, where in the graphical representation is generatedfurther based on the likelihood of failure for the at least one of thefunctionalities of the code base.

In another aspect combinable with any of the previous aspects, theoperations further include determining a failure pattern based on ananalysis of testing results prior testing to the code base, wherein theeach of the test cases in the set of test cases is further selectedbased on the failure pattern, and wherein the assigned priority valuefor each of the test cases in the set of test cases is furtherdetermined based on the failure pattern.

In another aspect combinable with any of the previous aspects, theoperations further include before receiving the log file, extractingterminologies from requirement documents; classifying the extractedterminologies into categories based on a corpus of known terms; andgenerating process maps based on the classified terminologies.

In another aspect combinable with any of the previous aspects, theoperations further include before selecting a set of test cases,clustering the plurality of test cases for the code base based oncontextual similarity.

In another aspect combinable with any of the previous aspects, theoperations further include before receiving the log file, receiving atest scenario and a context file selected through a user interface, thecontext file including an object map comprising objects that correlateto respective components of a display page for the code base, the testscenario describing one of the test cases involving an intendedinteraction with at least one of the components on the display page;correlating the intended interaction with the at least one componentwith the corresponding object in the object map; processing the intendedinteraction and the corresponding object through an AI model, the AImodel trained using training data comprising a plurality of processesand respective process steps supported by the components of the displaypage; determining a script template based on the processing and aselected automation tool; applying, based on the processing, the scripttemplate to the intended interaction and the correlated object togenerate an automated testing script for the selected automating tool;and assigning the generated automated testing script to the one of thetest cases.

In another aspect combinable with any of the previous aspects, theoperations further include before receiving the log file, determining acontextual distance for each of the test cases respective to each other;assigning each of the test cases to at least one cluster based on thecontextual distances; and assigning a score value to each test casesbased on a robustness to detect defects respective to the other testcases in the at least one cluster.

In another aspect combinable with any of the previous aspects, theoperations further include determining a resolution for a defect, thedefect reported based on a result of an execution a testing scriptassigned to one of the sequence set of test cases; and implementing theresolution in the code base.

Another aspect combinable with any of the previous aspects, thegraphical representation is a heat map.

In another aspect combinable with any of the previous aspects,processing the log file through the pattern-mining algorithm furtherdetermines a failure pattern, a business flow graph or an anomaly.

In another aspect combinable with any of the previous aspects, thegraphical representation depicts testing priorities of thefunctionalities of the code base.

In another aspect combinable with any of the previous aspects, the testcases are selected and assigned a priority based on a criterion.

In another aspect combinable with any of the previous aspects, thecriterion is a maximization of defect yield per test case executed.

In another aspect combinable with any of the previous aspects, themachine-learning algorithm is a reward-seeking algorithm trained tomaximize defect yield per test case.

In another aspect combinable with any of the previous aspects, each ofthe test scenarios is represented by a process graph that depicts ahierarchy of business process.

In another aspect combinable with any of the previous aspects, thecontextual distance for each test case includes a semantic distance foreach of the test cases respective to each other, a defect distance foreach of the test cases respective to each other, and a code hit distancefor each of the test cases respective to each other.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also may include any combination of the aspectsand features provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIGS. 1A-1C depict an example touchless testing platform system.

FIGS. 2A and 2B depict example implementations of the test scenario andprocess map extractor engine.

FIG. 3 depicts an example of the automation accelerator engine.

FIG. 4 depicts an example of the test suite analyzer engine.

FIG. 5 depicts an example of the log analyzer and pattern miner engine.

FIG. 6 depicts an example of test priority graphical representationgenerator engine.

FIG. 7 depicts an example of dynamic test case selector and sequencerengine.

FIG. 8 depicts an example of dynamic defect analyzer engine.

FIGS. 9A-9B depict flow diagrams of an example process employed within atouchless testing platform system.

FIG. 10 shows an example of a computing device and a mobile computingdevice.

DETAILED DESCRIPTION

Application and system testing includes a defined set of activitiespertaining to test case design, execution, and defect reporting. Withincreasing complexity of information technology (IT) architectures,increasing adoption of agile and DevOps, and quicker speed to market,testing practice expectations for overall performance and efficiency ofproduct releases is increased. Thus, testing methodologies to assure thego-live readiness in the shortest possible time of systems andapplication for release as well as improvements in the quality,stability, and reliability of these methodologies is beneficial.Accordingly, a need exists for widening the mandate for testing as wellas employing new strategies and techniques within the creation,maintenance, and execution of regression suites. For example,methodologies may be employed to create test cases based on a set ofcriteria, such as maximizing testing coverage. The methodologies maymeet the criteria by, for example, increasing automation and efficientreporting of discovered defects. Accordingly, a robust testing platformembraces speed and data driven intelligence, which enable users toeffectively and agility produce quality results.

In view of the foregoing, implementations of the present disclosure aregenerally directed to a touchless testing platform system for creatingautomated testing scripts, sequencing test cases, and implementingdefect solutions. The described system employs techniques, such as datamining, artificial intelligence, machine learning, and natural languageprocessing, to build and maintain enterprise testing suites. Beginningwith requirements documentation, the described system generates testscenarios, which are used to create test cases once the application orsystem has been built. Requirements documentation (e.g., businessrequirements, functional requirements, use cases, user stories, and soforth) captures, for example, information related to the businessprocess(es) that will be supported by the software, intended actionsthat will be performed through the software, managed data, rule sets,nonfunctional attributes (e.g., response time, accessibility, access andprivilege), and so forth. Using this documentation, the described systemcan be employed to extract key processes and events, featurerequirements, non-functional testing, and so forth through, for example,name entity recognition, topic segmentation, part-of-speech tagging,terminology and relationship extraction techniques, semantic clustering,and test case mind maps, which may be generated by employing, forexample, semantic graphs. Based on this extracted information, testscenarios are generated. Test scenarios explain, for example, a generalprocess without focusing on implementation. Once the design of thesoftware is available, the test scenarios can be converted into testcases specific to the implementation of the respective businessrequirement(s).

The described system provides benefits to test designers by quicklyidentifying the key attributes to be tested as well as their operationsand relationships, thus significantly improving the productivity andquality of test cases. For example, natural language processing (NLP)and clustering techniques can be employed to extract the intent from atest case and automatically create code. As an example, logging into anapplication or program, such as a web application, may require a username and password or completing a particular form in a web applicationwith a certain set of information. The described system may correlatethe intent of use with an object(s) on a selected page or screen of theweb application. Once the intent and objects are correlated, the systemselects a template(s) corresponding to a designated automation tool togenerate the automated testing script for the determined test scenarios.Once a test scenario has been translated to an automated testing script,the test scenarios can be tested frequently and used in a variety oftesting processes using the respective automated testing script.

Furthermore, testing automation may realize ‘unattended testing’ througha robust automation framework that provides for better coding practices,such as improved exception handling; portability across environments andapplications; and the integration of test data and deploymentenvironments into the automation logic. In some examples, test scriptsfail not because of a system failure, but due to environment failureand/or incorrect test data. Accordingly, the described system providesan orchestrator process to provide for the oversight of end-to-endexecution of regression suites. For example, the described system maygenerate alerts on failures that are due to the test environment and/orthe particular data used to test the functionality of the particularapplication or system being tested. These processes can also collectsystem logs and traces when a failure is encountered.

While it is important for the testing process to maximize coverage ofsystem requirements, the described system includes robust and optimizedtesting processes that search and discover defects. For example, oneobjective of testing optimization is to maximize defect yield inrelation to the amount of cost (e.g., time) spent on testing. Thedescribed system optimizes testing by employing AI and NLP techniquesto, for example, remove duplicate test cases and/or test casesexercising the same execution paths. NLP can be described as the abilityof a computer program or software to understand human speech as it isspoken or written. NLP makes it possible for an AI program to receiveconversational or text input by breaking the respective syntax down todetermine the input's meaning and/or determine an appropriate action.

Additionally, the described system optimizes testing by improving themaintainability and re-use of regression suites by identifying re-usablecomponents, such as test step and test cases that can be executedthrough other test cases.

Furthermore, graphical representations, such as heat maps, may becreated and employed for a quantitative analysis and selection of a setof test cases to execute. For example, a heat map may be created bydetermining system usage and failure patterns that may be extracted fromproduction logs where functionalities that have a higher usage and/or ahigher propensity to fail may indicate a condition for increased testpriority. Heat maps may also be used to determine functionalities wherechange is occurring at an increased rate. These types of factors mayalso indicate a condition for increased test priority. Additionally, thecode bases to be tested may be analyzed to set testing priorities basedon a determined quality of various modules within the code based.

The described system also provides processes for dynamic testsequencing. These processes select test cases to be executed based ontest priority, which may be determined according to the generatedgraphical representations. In some implementations, the order of testcase execution or test sequencing is dynamically determined to maximizevarious thresholds or criteria, such as defect yield per test caseexecuted. This can be done by, for example, clustering test cases basedon machine-learning algorithms that are trained according to previousexecution results.

The described system provides processes to increase test case vitality.The vitality of a test case includes its ability to activate faultchains, trigger failures, and/or detect defects. For example, the longera test case is in use, the more its vitality decreases due to factors,such as inadequate coverage of failure paths or improvements to thefunctionality that is covered by the test case. The described systemalso includes processes that conducted test vitality analysis to, forexample, remove test cases exhibiting poor vitality, thus improvingdefect yield per unit test case executed.

The described system also integrates functional and non-functionaltesting as a testing failure may happen due to functional and/ornon-functional root causes. For example, a functional defect mayoriginate from non-functional causes, such as a database timeout leadingto an incorrect update on a user interface (UI). In such an example,testers may tag the UI issue as a functional defect. To provide for thisintegration, the described system includes processes that analyzeperformance, scalability, stability, recoverability, exception handling,upgrade, and so forth, which are executed throughout the testing cycleand/or in parallel. Furthermore, the described system, uses applicationmonitoring and log mining to build useful insights into the underlyingarchitecture behavior as functional tests are being run. For example,thresholds for known problem patterns and monitor alerts may be set andmachine learning employed to determine typical system behavior as wellas search for anomalies.

The described system also includes processes that continuously builddata driven insights throughout the tested systems lifecycle. Theseinsights allow for the implementation of both corrective and preventionactions to, for example, achieve projects goals. Insights may includeusage patterns, such as how a system is being used, UI pathways beingexercised most often, and the control and data flow pathways that aremost prevalent; failure prediction and modeling, such as the systemcomponents most likely to fail, the most likely pathways that activateinternal fault chains and trigger failures, typical system and run timebehavior mining and anomaly detection, and anomalies that are prognosticof failures; churn patterns, such as the modules that are undergoingthat most churn, from where/who a change(s) is originating, the modulesundergoing burst changes; code and architectural quality, such aswhether churn is affecting code and/or architectural quality; defectanalysis, such as the likely root causes of defects; and fault chainrelationships, such as defects causally relationships to one another,root defects versus symptoms, and duplicates.

FIGS. 1A-1C depict an example touchless testing platform system 100. Theexample system 100 includes a control center 110, a touchless testingplatform module 120, and a data and artifact repository 130. In thedepicted example, the example touchless testing platform system 100 maybe deployed to any special purpose computer system. The example systemmay be provided using one or more computing device(s) of any suitablenumber and type of computing device. Configuration of such computingdevices include shared, virtual, managed/dedicated, cluster/grid, cloudbased resources, and/or any combination thereof.

The system 100 provides accesses to the users 112 through the controlcenter 110, which may be accessed over a network (not shown). In thedepicted system 100, a user 112 may analyze requirements for anapplication or system and then design, develop, and test software byapplying the theories and principles of computer science andmathematical analysis. The control center 110 includes a series ofscreens, which may be received and viewed on a user device using, forexample, a web browser program. Pages may be generated on a server orvirtual server and transmitted via a network. The control center 110allows the user(s) 112 to provide testing scenarios (e.g., test cases)and control commands to the various modules, which are described indetail below, included in the testing platform module 120 and to thedata and artifact repository 130. The control center 110 also allows theuser(s) 112 to analysis and view testing results.

The data and artifact repository 130 may be a relational database, suchas Oracle or DB2, implemented on an enterprise database server orvirtual server. The data and artifact repository 130 may store testingdata, such as system requirements 131, which include requirements forthe application or system that is being tested; defect and pastresolution data 132, which includes defects reported and the respectiveresolution(s); configuration and control data 133, which includesconfigurations for the tested application or systems; production data134, which includes data from the production or other deploymentenvironments for the tested system or application; test cases 135, whichinclude test cases for the function features of the tested applicationor system; test results 136, which include the results of the regressionsuites and various test scripts; application logs 137, which includelogs from the various deployment environments for the tested applicationor system; trace data 138, which includes information about the testedapplication or system's execution; and code quality analysis data 139,which includes results of quality analysis for the tested application orsystem. These various types of testing data 131-139 are provided asexamples. Other types of testing data may be stored in the data andartifact repository 130 and used by the control center 110 and/or thetesting platform module 120.

The testing platform module 120 includes test scenario and process mapextractor engine 121, automation accelerator engine 122, test suiteanalyzer engine 123, log analyzer and pattern miner engine 124, testpriority graphical representation generator engine 125, dynamic testcase selector and sequencer engine 126, and defect analyzer engine 127.The testing platform module 120 achieves a seamless automated workflowof a testing lifecycle guided by data driven intelligence. This datadriven intelligence is achieved by AI and machine-learning techniquesthat accelerate the automation of the activities and the decision makingat various stages of testing. The testing platform module 120 integratesthese modules as well as third-party and open source tools.

The test scenario and process map extractor engine 121 scans therequirements document and creates the high level test scenarios andprocess maps. The automation accelerator engine 122 analyzes manual testcases, extracts the intent, and converts the intent into executableautomated scripts. The test suite analyzer engine 123 analyzes testsuite and groups contextually similar test cases into clusters based oncontextual distance. The log analyzer and pattern miner engine 124ingests the log files from deployment environments, such as productionor test environment and extracts various insights, such as usage and/orfailure patterns, typical system behaviors, and/or anomalies (which inturn can be used as early warning of potential failures). The testpriority graphical representation generator engine 125 generatesgraphical representations, such as heat maps, that depict the testpriority of various functionalities. The graphical representation may begenerated based on usage patterns, failure patterns, module churn, codequality analysis, or a combination thereof. The dynamic test caseselector and sequencer engine 126 selects a next set of test cases to beexecuted based on a configurable criterion. The defect analyzer engine127 analyzes defect data and provides data driven insights. Each ofthese engines are described in greater detail below with regard to FIGS.2A-9.

FIG. 2A depicts an example 200 of the test scenario and process mapextractor engine 121, which includes terminology extractor module 210,business process classifier module 220, operations classifier module222, data set classifier module 224, business process map builder module230, test scenario map builder module 232, and non-functional attributebuilder module 234. As described above with regard to FIG. 1, the testscenario and process map extractor engine 121 scans the requirementsdocument and creates the high level test scenarios and process maps thatcan be employed by the user 122, such as a test designer, to create testsuites.

Terminology extractor module 210 extracts the various terminologies fromthe requirements document by using techniques, such as terminologyextraction. For example, the terminology extractor module 210 may pickup key terminologies pertaining to business processes, operations,and/or data and control. This information is fed into, for example, oneof the three classifier modules: business process classifier module 220,operations classifier module 222, and data set classifier module 224.Each of the three classifiers may classify the received terms into, forexample, business processes, operations and actions, or data sets byusing techniques, such as topic segmentation. The business processclassifier module 220 classifies terminology pertaining to, for example,a business process described in the requirements document. Theoperations classifier module 222 classifies terminology pertaining to,for example, business operations applicable to business processesclassified by the business process classifier module 220. The data setclassifier module 224 classifies data and control informationrequirements to perform the operations classified by the operationsclassifier module 222. Each of the three classifier modules may also useentity term corpus 151, which is stored in data and artifact repository130, as per testing requirements. The entity term corpus 151 includesbusiness process terms, operations terms, technology terms, and data setterms that are domain specific and/or related to the scope of therespective requirements documentation. Once classified, the data is fedinto the three builder modules. Three classifier modules are shown anddescribed; however, other classifier modules may be employed in the testscenario and process map extractor engine 121.

The process map builder module 230 builds a process map (e.g., graph)using techniques, such as relationship extraction and/or semanticgraphs. The process map may depict, for example, a hierarchy of businessprocesses. The test scenario map builder module 232 builds a testingscenario using techniques, such as knowledge representation. The testingscenario can be depicted using a knowledge graph that may depict, forexample, test scenarios, operations applicable to a respective process,and data sets required for such operations. The non-functional attributebuilder module 234 identifies the requirements that need non-functionalattributes (e.g., quality attributes) and extracts those attributesusing techniques, such as relationship extraction. This information ispassed to the users 122 by way of the control center 110.

FIG. 2B depicts another example 250 of the test scenario and process mapextractor engine 121, which includes terminology extractor module 260,test scenario element classifier module 270, semantic graph buildermodule 280, process map builder module 282, and test scenario mapbuilder and quality attribute extractor module 290. As described abovewith regard to FIGS. 1 and 2A, the test scenario and process mapextractor engine 121 scans the requirements document and creates thehigh-level test scenarios and business process maps that can be employedby the user 122, such as a test designer, to create test suites.

In the depicted example 200, terminology extractor module 210 extractsterms 262 from requirements documentation. Operations classifier module270 parses these extracted terms 262 and classifies them as/according toprocess terms 272, operations terms 274, and data set terms 276.Semantic graph builder module 280 uses the classified terms to constructa semantic graph. Process map builder module 282 uses the semanticgraphs to construct process maps 284. Test scenario map builder 290 usesthe generated semantic graphs and process maps to generate test scenariomaps 292.

Terminology extractor module 260 is substantially similar to terminologyextractor module 210 from FIG. 2A. Additionally, terminology extractormodule 260 parses requirements documents stored in data and artifactrepository 130 or otherwise received from user 112. Terminologyextractor module 260 extracts key terminologies, extracted terms 262,pertaining to processes (e.g., business or system processes performed bya developed application), operations, actions, and flow and control ofdata. In some implementations, terminology extractor module 260identifies a single word, double words, or multi-word terminologiesbased on the entity term corpus 151, which is stored in data andartifact repository 130. As stated above, the entity term corpus 151includes business process terms, operations terms, technology terms, anddata set terms that are domain specific and/or related to the scope ofthe respective requirements documentation. The extracted terms 262identified or learned by terminology extractor module 260 formrequirements documentation may include new terms. These new terms may beadded to the entity term corpus 151, as shown in FIG. 2B.

Test scenario element classifier module 270 classifies the extractedterms 262 into process terms 272, operations terms 274, and data setterms 276 by employing techniques, such as, topic segmentation and soforth. Process terms 272 include terminology pertaining to, for example,a business process identified in the requirements documentation.Operations terms 274 include, for example, business operationsapplicable to the identified business processes. Data set terms 276include information regarding requirements to perform the identifiedoperations, such as what data a particular form requires or what type ofdata is needed for a parsing script to execute. Test scenario elementclassifier module 270 may employ a corpus of existing known terms toassist in identifying terms and classifying them accordingly.

Semantic graph builder module 280 processes classified terms toconstruct standardized, grammatically accurate, and non-ambiguousstatements. For example, a requirement document may include languagestating “The Foreign currency accounting modules should be able toproduce general forex voucher which can support following 3 types oftransactions: debit advice voucher, forex receipt voucher, and currencyswapping voucher.” A semantic graph for this line of requirement builtfor this information may include a node for “Foreign currencyaccounting” that is linked to a “general forex voucher” node, which islinked to nodes for “debit advice voucher,” “forex receipt voucher,” and“currency swapping voucher.”

Process map builder module 282 employs the semantics graphs generated bysemantic graph builder module 280 to generate process flow maps 284. Aprocess flow map includes information as to how various processes arerelated to each other. Such as, how processes are hierarchicallyrelated. Example process flows include business processes that therespective application integrates with or manages. Other examplesprocess flows include data flow processes, functionality, workflows,blockchains, and so forth. Each generated process flow map 284 may alsoinclude details of regarding operations and flow and control of datarelated to the respective process. The generated process flow maps maybe stored in process map repository 286. In some implementations, user112 reviews the generated process maps to update the information and/ormerge them into the other data contained in the process map repository286.

Test scenario map builder 290 uses the generated semantics graphs andprocess maps to generate test scenarios maps 292 for the respectiverequirements documentation. The semantics graphs and process mapsinclude, based on the requirements documentation, processes andfunctionality that may be tested for an application, valid and invalidoperations for each functionality, expected outputs, and therelationships, such as a hierarchically relationship, between thevarious processes.

The generated test scenario graphs may include, nested test scenarios.These scenarios (or groups of scenarios) can be reused across multiplerequirements and applications. For example, login into the application”is a scenario that can be used in a “search for product” scenario andthe “search for product” scenario may be part of a “compare products”scenario. The test scenario map builder 290 modularizes the requirementsstatements from a semantic graph(s) and converts them into completestatements through the use of, for example, NLP. For example,requirements statements may be parsed to extract Intent, Objects,Actions, and/or expected results. The test data elements are alsoidentified where available. Combinations of these are then used by thetest scenario map builder 290 to build the complete statements. Testscenario map builder 290 then classifies functional and non-functionaltest scenarios. The functional scenarios may be used by the automationaccelerator engine 122 to generate automation test scripts. Thenon-functional scenarios may be employed by a testing team(s) togenerate test cases specific to their respective areas, such asperformance, security, and architectural testing. In someimplementations, the test scenario map builder 290 includes anon-functional attribute builder that scans the requirements documentand extract requirements that are likely to have performance andscalability requirements. These non-functional attributes, such asresponse time, concurrent user limits, wait time, page, component loadtime, and so forth, are identified along with the specified values.These attributes are employed to build, for example, performance andload testing models. The generated test scenarios stored in data andartifact repository 130 are used by the automation accelerator engine122, as described below.

FIG. 3 depicts an example 300 of the automation accelerator engine 122,which includes NLP engine 310, application object scanner module 320,object correlator module 330, and script generator module 340. The NLPengine 310 includes parser module 312 and intent extractor module 314.As described above with regard to FIG. 1, the automation acceleratorengine 122 analyzes manual test cases, extracts the intent, and convertsthe intent into executable automated scripts.

For example, the automation accelerator engine 122 generates anautomated testing script(s) from a provided test scenario and contextfile. The automation accelerator engine 122 extracts the intendedinteraction (intent) and relevant testing data from each test scenariothrough the employment of, for example, natural language processing(NLP) techniques. The intent is correlated to an appropriate testobject(s) in the provided context file. For example, if the testscenario recites “Click on the Submit button,” the automationaccelerator engine 122 parses the natural language and derives thecontext as “submit button,” which it then maps to the submit buttonobject from the object map of the submitted context file. A template forthe selected automation tool is applied to the extracted intent and dataalong with the correlated object(s) to generate the resulting automatedtesting script.

At a high level, the NLP Engine 310 employs NLP to parse and extract theintent from manual test cases. The object correlator module 330 createsthe logical objects in accordance with the extracted intent, which itmaps to objects in the context file. The application object scannermodule 320 scans the tested application or system and identifies theobjects within, for example, the UI pages. The script generator module340 generates the test scripts bases on the input from object correlatormodule 330.

For example, test cases (e.g., test scenarios) may be described in afree flow language form, without any well-defined format, and in thenatural or agreed upon language of the parties, such as English. Thus,it is difficult for a computer program to translate this informationinto an automated testing script. Moreover, development projects areincreasingly using test scenarios written in a behavior-drivendevelopment (BDD) style using a formatted language, such as Gherkin. Theformatted language allows for instructions to be written as plain textin a traditional written language, such as English or one over 60 otherlanguages, with some additional structure. These formatted languages aredesigned to be easy to learn by non-programmers, yet structured enoughto allow concise descriptions of examples to illustrate business rulesin most real-world domains. In this way, the formatted languages can beused to capture requirements and define the test scenarios.

By way of example, a test scenario or list of scenarios may be includedin what is called a feature file, where a formatted language, such asGherkin, is used to write the scenarios in a human readable way. Such afeature file may be used in the generation of an automated testingscript for an automation tool. Example automation tools include UnifiedFunctional Testing (UFT), Tricentis Tosca™, Worksoft Certify™, andSelenium™. The testing automation tool provides a framework that can beused to provide support software structures, such as step definitions,for each of the test scenarios. Step definitions act as skeletonplaceholders where automation code blocks may be implemented. Forexample, each step in a given scenario may map to a step definition. Theautomation code block is implemented for each step definition andexecuted when the scenario is run by the testing framework. Theautomation code block may be written in a variety of programminglanguage, such as Ruby, C++, Java, Scala, Python, and so forth, selectedbased on system requirements. Once generated, the step definitions andrespective code blocks, may be referred to as an automated testingscript. The testing automation tool provides an execution environmentfor these generated scripts, which may be run for acceptance and/orregression testing.

The automated testing scripts may be implemented manually by a developeror generated automatically. One of the difficulties with automatic codegeneration of an automated testing script from a test scenario is thatthe test scenario may be written in many different ways because of theflexible nature of the formatted language (e.g., Gherkin). For example,each tester may describe the functionality or function of a test in hisor her own way. For instance, “click on the submit button,” “push thesubmit button,” or “hit the submit button,” all of which mean the samething. Another issue is that the code blocks in an automated testingscript may be repetitive, hence, the automation code may become bloated.Additionally, the free form English (or any other language), which maybe used in the bulk of the file, lacks structure. For example, “Login tothe application” and “Enter the user name, enter the password, and clicksign in button” both denote the same function; one is a single sentence,but the other is three sentences. However, the same automation codeblock (script) should be generated in each of these examples.

The automation accelerator engine 122 parses a provided test scenariobase on natural language processing techniques to determine thescenario's intent for the code base that is being testing along with anydata relevant to the scenario. As an example, a scenario's intent may beto login to a given application or program, such as a web application,with a username and password or to complete a particular form in the webapplication with a certain set of information. In the proceeding examplethe username and password and the set of information are the datarelevant to the scenario. The system correlates the intent with anobject(s) on a selected page or screen of the web application. Once theintent and objects are correlated, the system selects a template(s)corresponding to a designated automation tool to generate the automatedtesting script for the provided scenario. Once the test scenario hasbeen translated to an automated testing script, the test scenarios canbe tested frequently and used in a variety of testing processes throughthe use of the respective automated testing script. Additionally, theautomated testing script may be more precise than the manual versionsand may also be used in the generation of reports regarding respectiveapplication and/or the results of the testing.

In the depicted example, the NLP engine 310 receives a test scenario(s)from the data and artifact repository 130, and optionally, a contextfile from the control center 110. The received test scenarios may begenerated by the test scenario and process map extractor 121 engine asdescribed above. The test scenarios may also include existing testcases, feature files, API definition files, such as Web ServicesDescription Language (WSDL), Web Application Description Language(WADL), Swagger, and so forth. The NLP engine 310 receives the input andparses the test scenarios. NLP can be described as the ability of acomputer program or software to understand human speech as it is spokenor written. The NLP engine 310 may employ an NLP application programinterface (API), such as Apache OpenNLP™

As an example, the parser module 312 reads a line or set of lines fromthe received test scenario, which may be included in a feature file. Theparser module 312 determines the various objects in the sentence(s) inthe feature file. The objects are used to provide a context for theinstructions in the test scenario. Based on this information, the intentextractor module 314 determines the intent of the scenario for each ofthe identified objects. For example, the scenario might read “to login,to enter a username, to enter a password, to click on submit, enternavigation.” The intent extractor module 314 extracts the variousintents for this scenario (e.g., “click,” “login,” and “enter”). Oncethe intent has been identified, it may be correlated to the object(s) inthe context file by the object correlator module 330 based the contextfile received from the application object scanner module 320 andselected through the control center 110 by users 112.

Custom actions can also be trained into the intent parser module 312 forcomplex descriptions. The intent extractor module 314 also identifies,for each determined intent, any associated data, for example, a usernameand password. Techniques such as text parsing, tokenizer, and namefinder can be used to identify the mention of custom data within thetest scenario as data specific words may be mentioned in any part of asentence.

The object correlator module 330 takes the identified intent and anyassociated data and correlates the information to objects within thecontext file. The object correlator module 330 first generates a namefor the intent based on the NLP processing. The name is based on thecontext provided in the test scenario, and is referred to as a logicalname. The object correlator module 330 searches the object map in thecontext file to determine the best matching object. For example, a testscenario regarding a web application selling various products may have amovie store section. A test scenario, for example, a login to the moviestore, may refer to the section of the site as “DVD movie store.” Theintent extractor module 314 may determine the intent for the scenario as“enter the DVD movie store,” where “DVD movie store” is the logical nameassigned to the object. The object correlator module 330 takes theassigned logical name and searches the object map from the context filefor an object that has an identifier that matches the logical name or isthe closest match to the object's name. This match is determined basedon the determined intent, NLP of the objects, and any corresponding datain the context file (e.g., identifier and/or associated parameters). Theobject correlator module 330 correlates the intent (e.g., login, enter,click) to the matched object from the context file. For the movie storeexample, the object correlator module 330 may return an objectcorresponding to a button or link that takes the user to the DVD moviestore section of the site, which is then correlated with the intent oraction of the scenario (e.g., click on the movie store link).

The script generator module 340 generates an automated testing scriptbased on the determined intent and the associated data from the providedscenario and the correlated object(s) from the provided context file.For example, the determined intent could be at an elementary level(click, enter) or a business process level (login, fill out a form). Alogin may require a series of steps, such as 1) enter user name, 2)enter password, and 3) click sign on. Another example may be to create apurchase order, which could entail filling out a form with multiplesteps.

To generate the file, the script generator module 340 may employ an AImodel trained through a series of machine-learning techniques applied toan algorithm using these elementary and business level steps. Machinelearning automates model building and allows for the discovery ofinsights without being explicit programming. Using automated anditerative algorithms, models may be trained to find high-orderinteractions and patterns within data. The AI model may be trained for aparticular application or program to apply action(s) required tocomplete various tasks or processes within the application. For example,an AI model may be trained to understand what a purchase order means,what login means, and how to perform each within the particularapplication or program. In some implementations, the provided contextfile is used to determine the appropriate AI model to employ to buildthe resulting automated testing script.

The script generator module 340 selects a script template(s) from thedata and artifact repository 130. In some implementations, the scripttemplates are a standardized form for automation, which may be employedin keyword driven, data driven and/or hybrid automation frameworks. Insome implementations, the script templates are standard or proprietarytemplates defined by an automation tool, such as Tricentis Tosca™. Thescript template is selected based on the automation tool for which theautomated testing script is being generated (e.g., UFT, TricentisTosca™, Worksoft Certify™, or Selenium™). Based on the AI model, thescript generator module 340, determines the action(s) to perform thedetermined intent to the correlated objects in the respective page ofthe UI being tested. The script generator module 340 generates theautomated script by applying the selected template to the determinedactions for the intent and correlated objects, the data read from theprovided test scenario, and the step definitions from the test scenario(e.g., the feature file). The data may also be read from a configurationor properties file. This data may be used as the default data unlessspecific information is provided in the test scenario. In someinstances, the actions may not require any additional data, such as, forexample, when simply following a link or clicking a button of aparticular page within the UI.

FIG. 4 depicts an example 400 of the test suite analyzer engine 123,which includes contextual distance calculator module 410, aggregatesimilarity cluster builder module 420, and vitality analyzer module 430.As described above with regard to FIG. 1, test suite analyzer engine 123analyzes test suites and groups contextually similar test cases intoclusters based on contextual distance. These clusters enable the user112, such a test analyst, to identify duplicate test cases and also helpin optimizing test suites and their execution.

The contextual distance calculator module 410 calculates the contextualdistance between a test case and other test case. The contextualdistance calculator module 410 includes text and semantic distancecalculator module 412, which determined the similarities of test casesbased on, for example, textual content and semantics; defect distancecalculator module 414, which determines the similarities of test casesbased on, for example, the defects each respective test cases isdetecting; and the code hit distance module 416, which determines thesimilarities of test cases based on, for example, the portions of codebeing executed by the test cases. The aggregate similarity clusterbuilder module 420 receives the calculated contextual distanceinformation regarding the test cases and constructs similarity clusters.The vitality analyzer module 430 then takes the clustered data toanalyze each test case and its robustness to detect defects based on,for example, past history. For example, each test case may be assigned ascore value based on its robustness to detect defects respective to theother test cases in an assigned cluster. This information is passed tothe users 122 by way of the control center 110.

FIG. 5 depicts an example 500 of the log analyzer and pattern minerengine 124, which includes log qualifier module 510, algorithm selectormodule 520, pattern analyzer module 530, metadata creator module 540,data extractor module 550, and report generator 560. As described abovewith regard to FIG. 1, the log analyzer and pattern miner engine 124ingests and analyzes the logs from deployment environments, such asproduction or test environment and extracts various insights, such asusage and/or failure patterns, typical system behaviors, anomalies(which in turn can be used as early warning of potential failures).

The log qualifier module 510 qualifies the collected logs from varioussources, such as application logs, server logs and UI logs. Thealgorithm selector module 520 receives the qualified logs from logqualifier module 510 and selects a pattern-mining algorithm based on theinitial qualification. The pattern analyzer module 530 receives theselected pattern and qualified logs to analyzes patterns present in thelogs. The metadata creator module 540 receives the information regardingpatterns from the pattern analyzer module 530 and creates metadata foreach of the pattern groups. The data extractor module 560 receives theinformation from the prior modules and extracts the various data ofinterest from the identified patterns. The report generator module 560receives the determined information from the other modules and generatesreports on, for example, usage patterns, failure patterns, business flowgraphs, and anomalies. This information is passed to the test prioritygraphical representation generator engine 125.

FIG. 6 depicts an example 600 of test priority graphical representationgenerator engine 125, which includes module churn analyzer module 610,test results analyzer module 620, code quality analyzer module 630,failure prediction module 640, and graphical representation generatormodule 650. As described above with regard to FIG. 1, the test prioritygraphical representation generator engine 125 generates graphicalrepresentations, such as heat maps, that depicts the test priority ofvarious functionalities based on usage patterns, failure patterns,module churn, and code quality analysis.

The module churn analyzer module 610 continuously analyzes configurationand control as well as module churn data 133. The test results analyzermodule 620 continuously analyzes the results of execution runs 135 andextracts failure patterns. The code quality analyzer module 630 analyzesstatic code quality based on preset rules. The static code can bereceived from code repository 152, which can be stored in data andartifact repository 130. The failure prediction module 640 receives theextracted and analyzed information from modules 610-630 and employsmachine-learning algorithms to predict which functionalities in thetested application or system are likely to fail. The graphicalrepresentation generator module 650 generates graphical representationdepicting relative test priorities of functionalities and associatedtest cases based on the prediction from module 640 and the receivedinformation from modules 610-640. The generated graphicalrepresentations are passed to the dynamic test case selector andsequencer engine 126.

FIG. 7 depicts an example 700 of dynamic test case selector andsequencer engine 126, which includes test case selector module 710, testcase cluster builder module 720, test results analyzer 730, and testcase sequencer module 740. As described above with regard to FIG. 1, thedynamic test case selector and sequencer engine 126 selects a next setof test cases to be executed based on a configurable criterion, such asmaximization of defect yield per test case executed.

The test case selector module 710 selects test cases to be executed fromtest priority graphical representations received from the test prioritygraphical representation generator engine 125. The test case clusterbuilder module 720 clusters the selected test cases based on contextualsimilarity. The test results analyzer 730 continuously analyzes theresults of execution runs and extracts failure patterns 136. Test casesequencer module 740 receives the selected and clustered test cases aswell as the execution results and uses, for example, reward seekingmachine-learning algorithms to set execution sequence of the clusteredtest case based on a configurable criterion, such as to maximize defectyield per test case. This sequence of test cases is passed to the testexecution engine 140.

The test execution engine 140 includes an auto-script server that runsthe automation tools/platforms, such as Unified Functional Testing(UFT), Tricentis Tosca™, Worksoft Certify™, or Selenium™, employedwithin the touchless testing platform system 100. Such automation toolsare used to provide stakeholders with information about the quality ofthe application or system being tested. The test execution engine 140may be deployed on a server or virtual server.

FIG. 8 depicts an example 800 of dynamic defect analyzer engine 127,which includes similarity cluster builder module 810 and defect analyzermodule 820. As described above with regard to FIG. 1, the dynamic defectanalyzer engine 127 analyzes the defect found through the execution ofthe sequence test case and other test cases run against the testedapplication or system. The dynamic defect analyzer engine 127 determinesrecommendation resolutions and executes determined resolutions based onconfigurable criteria, such as threshold events.

The similarity cluster builder module 810 clusters defects 132 based onsimilarity analytics. The defect analyzer module 820 classifies theclustered defects based on an AI module trained through machine learningwith, for example, past resolution data. This determined recommendationand results of executed resolutions are passed users 112 by way of thecontrol center 120. In some implementations, the dynamic defect analyzerengine 127 may determine, through the defect analyzer module 820, aresolution for a defect reported based on a result of an execution of atesting script assigned to one of the sequence set of test cases andimplement the resolution in the code base for the application or systemto be tested.

The control center 120 may provide this information to the users 112through a reporting engine which provides analytics and access to thereporting features. The execution engine 160 may persist results fromthe execution of the generated automated testing scripts in a reportingdatabase (not shown). The reporting engine may generate reports from theinformation stored in the reporting database, which can be reviewed byusers 112. These reports provide details on the performance of thesystem during the execution of the automated testing scripts and mayinclude processing time, response time, and any warning messagesdisplayed as well as the information generated by the various engines131-137 of the touchless testing platform module 120. Statisticalreports may also be generated that summarize the health of the system aswell as the any significant data points selected.

FIG. 9A depicts a flow diagram of an example process 900 employed withina touchless testing platform system, such as touchless testing platformsystem 100, to generate a sequenced set of test cases for execution byan execution engine, such as execution engine 140. A log analyzer andpattern miner engine receives (902) a log file that includes log recordsgenerated from a code base. The log file is processed (904) by the loganalyzer and pattern miner engine through a pattern-mining algorithm todetermine a usage pattern. A graphical representation, such as a heatmap, is generated (906) by a test priority graphical representationgenerator engine based on an analysis of the usage pattern. A set oftest cases is selected (908) and each of the selected test cases isassigned (908) by a dynamic test case selector and sequencer engine byprocessing the graphical representation through a machine-learningalgorithm. The set of test cases is sequenced (910) by the dynamic testcase selector and sequencer engine module based on the assigned priorityvalues. The sequenced set of test cases are transmitted (912) to thetest execution engine for execution and the process ends.

FIG. 9B depicts a flow diagram of an example process 920 employed withina touchless testing platform system, such as touchless testing platformsystem 100, to provide a test scenario map to a tester, such as user112, though a UI. A test scenario and process map extractor receives(922) requirements documentation for a respective application or systemthrough a UI. The requirements documentation is analyzed (924) by aterminology module to extract terminologies based on an entity termcorpus, which are categorized by a test scenario element classifierbased on a corpus of known terms. A semantic graph is generated (926)from standardized statements constructed from the categorized extractedterminologies. A process flow map for the application or system isgenerated (928) by identifying processes of the application or systemand a respective relationship between each process from the semanticgraph. A test scenario map of test scenarios for the application isgenerated (930) from the process flow map and the semantic graph. Thetest scenario map is provided (932) to a tester through a UI and theprocess ends.

FIG. 10 shows an example of a computing device 1200 and a mobilecomputing device 1250 that can be used to implement the techniquesdescribed here. The computing device 1000 is intended to representvarious forms of digital computers, such as laptops, desktops,workstations, personal digital assistants, servers, blade servers,mainframes, and other appropriate computers. The mobile computing device1050 is intended to represent various forms of mobile devices, such aspersonal digital assistants, cellular telephones, smart-phones, andother similar computing devices. Additionally, computing device 1000 or1050 can include Universal Serial Bus (USB) flash drives. The USB flashdrives may store operating systems and other applications. The USB flashdrives can include input/output components, such as a wirelesstransmitter or USB connector that may be inserted into a USB port ofanother computing device. The components shown here, their connectionsand relationships, and their functions, are meant to be examples only,and are not meant to be limiting.

The computing device 1000 includes a processor 1002, a memory 1004, astorage device 1006, a high-speed interface 1008 connecting to thememory 1004 and multiple high-speed expansion ports 1010, and alow-speed interface 1012 connecting to a low-speed expansion port 1014and the storage device 1006. Each of the processor 1002, the memory1004, the storage device 1006, the high-speed interface 1008, thehigh-speed expansion ports 1010, and the low-speed interface 1012, areinterconnected using various buses, and may be mounted on a commonmotherboard or in other manners as appropriate. The processor 1002 canprocess instructions for execution within the computing device 1000,including instructions stored in the memory 1004 or on the storagedevice 1006 to display graphical information for a GUI on an externalinput/output device, such as a display 1016 coupled to the high-speedinterface 1008. In other implementations, multiple processors and/ormultiple buses may be used, as appropriate, along with multiple memoriesand types of memory. Also, multiple computing devices may be connected,with each device providing portions of the necessary operations (e.g.,as a server bank, a group of blade servers, or a multi-processorsystem).

The memory 1004 stores information within the computing device 1000. Insome implementations, the memory 1004 is a volatile memory unit orunits. In some implementations, the memory 1004 is a non-volatile memoryunit or units. The memory 1004 may also be another form ofcomputer-readable medium, such as a magnetic or optical disk.

The storage device 1006 is capable of providing mass storage for thecomputing device 1000. In some implementations, the storage device 1006may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 1002), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 1004, the storage device 1006, or memory on theprocessor 1002).

The high-speed interface 1008 manages bandwidth-intensive operations forthe computing device 1000, while the low-speed interface 1012 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 1008 iscoupled to the memory 1004, the display 1016 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 1010,which may accept various expansion cards. In the implementation, thelow-speed interface 1012 is coupled to the storage device 1006 and thelow-speed expansion port 1014. The low-speed expansion port 1014, whichmay include various communication ports (e.g., USB, Bluetooth, Ethernet,wireless Ethernet) may be coupled to one or more input/output devices.Such input/output devices may include a scanner 1030, a printing device1034, or a keyboard or mouse 1036. The input/output devices may also bycoupled to the low-speed expansion port 1014 through a network adapter.Such network input/output devices may include, for example, a switch orrouter 1032.

The computing device 1000 may be implemented in a number of differentforms, as shown in the FIG. 10. For example, it may be implemented as astandard server 1020, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 1022. It may also be implemented as part of a rack serversystem 1024. Alternatively, components from the computing device 1000may be combined with other components in a mobile device, such as amobile computing device 1050. Each of such devices may contain one ormore of the computing device 1000 and the mobile computing device 1050,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 1050 includes a processor 1052, a memory1064, an input/output device such as a display 1054, a communicationinterface 1066, and a transceiver 1068, among other components. Themobile computing device 650 may also be provided with a storage device,such as a micro-drive or other device, to provide additional storage.Each of the processor 1052, the memory 1064, the display 1054, thecommunication interface 1066, and the transceiver 1068, areinterconnected using various buses, and several of the components may bemounted on a common motherboard or in other manners as appropriate.

The processor 1052 can execute instructions within the mobile computingdevice 1050, including instructions stored in the memory 1064. Theprocessor 1052 may be implemented as a chip set of chips that includeseparate and multiple analog and digital processors. For example, theprocessor 1052 may be a Complex Instruction Set Computers (CISC)processor, a Reduced Instruction Set Computer (RISC) processor, or aMinimal Instruction Set Computer (MISC) processor. The processor 1052may provide, for example, for coordination of the other components ofthe mobile computing device 1050, such as control of UIs, applicationsrun by the mobile computing device 1050, and wireless communication bythe mobile computing device 1050.

The processor 1052 may communicate with a user through a controlinterface 1058 and a display interface 1056 coupled to the display 1054.The display 1054 may be, for example, a Thin-Film-Transistor LiquidCrystal Display (TFT) display or an Organic Light Emitting Diode (OLED)display, or other appropriate display technology. The display interface1056 may comprise appropriate circuitry for driving the display 1054 topresent graphical and other information to a user. The control interface1058 may receive commands from a user and convert them for submission tothe processor 1052. In addition, an external interface 1062 may providecommunication with the processor 1052, so as to enable near areacommunication of the mobile computing device 1050 with other devices.The external interface 1062 may provide, for example, for wiredcommunication in some implementations, or for wireless communication inother implementations, and multiple interfaces may also be used.

The memory 1064 stores information within the mobile computing device1050. The memory 1064 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 1074 may also beprovided and connected to the mobile computing device 1050 through anexpansion interface 1072, which may include, for example, a Single inLine Memory Module (SIMM) card interface. The expansion memory 1074 mayprovide extra storage space for the mobile computing device 1050, or mayalso store applications or other information for the mobile computingdevice 1050. Specifically, the expansion memory 1074 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 1074 may be provided as a security module for themobile computing device 1050, and may be programmed with instructionsthat permit secure use of the mobile computing device 1050. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or non-volatilerandom access memory (NVRAM), as discussed below. In someimplementations, instructions are stored in an information carrier. Theinstructions, when executed by one or more processing devices (forexample, processor 1052), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices, such as one or more computer- or machine-readablemediums (for example, the memory 1064, the expansion memory 1074, ormemory on the processor 1052). In some implementations, the instructionscan be received in a propagated signal, for example, over thetransceiver 1068 or the external interface 1062.

The mobile computing device 1050 may communicate wirelessly through thecommunication interface 1066, which may include digital signalprocessing circuitry where necessary. The communication interface 1066may provide for communications under various modes or protocols, such asGlobal System for Mobile communications (GSM) voice calls, Short MessageService (SMS), Enhanced Messaging Service (EMS), or Multimedia MessagingService (MMS) messaging, code division multiple access (CDMA), timedivision multiple access (TDMA), Personal Digital Cellular (PDC),Wideband Code Division Multiple Access (WCDMA), CDMA2000, or GeneralPacket Radio Service (GPRS), among others. Such communication may occur,for example, through the transceiver 1068 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth, Wi-Fi, or other such transceivers. In addition, a GlobalPositioning System (GPS) receiver module 1070 may provide additionalnavigation- and location-related wireless data to the mobile computingdevice 1050, which may be used as appropriate by applications running onthe mobile computing device 1050.

The mobile computing device 1050 may also communicate audibly using anaudio codec 1060, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 1060 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 1050. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 1050.

The mobile computing device 1050 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 1080. It may also be implemented aspart of a smart-phone, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be for a special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical UI or a Web browser through which a user can interactwith an implementation of the systems and techniques described here), orany combination of such back end, middleware, or front end components.The components of the system can be interconnected by any form or mediumof digital data communication (e.g., a communication network). Examplesof communication networks include a local area network (LAN), a widearea network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications are possible. For example, while a clientapplication is described as accessing the delegate(s), in otherimplementations the delegate(s) may be employed by other applicationsimplemented by one or more processors, such as an application executingon one or more servers. In addition, the logic flows depicted in thefigures do not require the particular order shown, or sequential order,to achieve desirable results. In addition, other actions may beprovided, or actions may be eliminated, from the described flows, andother components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A computer-implemented method executed by one ormore processors, the method comprising: receiving a log file thatincludes log records generated from a code base; processing the log filethrough a pattern-mining algorithm to determine at least one insight;generating a graphical representation based on an analysis of the atleast one insight; processing the graphical representation through amachine-learning algorithm to select a set of test cases from aplurality of test cases for the code base and to assign a priority valueto each of the selected test cases; sequencing the set of test casesbased on the priority values; and transmitting the sequenced set of testcases to a test execution engine.
 2. The method of claim 1, wherein thegraphical representation is a heat map.
 3. The method of claim 1,wherein the at least one insight includes a failure pattern, a businessflow graph or an anomaly.
 4. The method of claim 3, further comprising:generating a report based on the at least one insight; and transmittingthe report to a control center application.
 5. The method of claim 1,further comprising: the at least one insight being a usage pattern;analyzing the usage pattern to identify pattern groups; and generatingmetadata for the identified pattern groups.
 6. The method of claim 1,further comprising, determining churn data based on an analysis ofconfiguration and coding changes to the code base; processing testresults, the churn data, and the code base through an ArtificialIntelligence (AI) model, the AI model trained using training datacomprising resolutions to reported defects for the code base; anddetermining a likelihood of failure for at least one of functionalitiesof the code base, where in the graphical representation is generatedfurther based on the likelihood of failure for the at least one of thefunctionalities of the code base.
 7. The method of claim 6, wherein thegraphical representation depicts testing priorities of thefunctionalities of the code base.
 8. The method of claim 1, wherein thetest cases are selected and assigned a priority based on a criterion. 9.The method of claim 8, wherein the criterion is a maximization of defectyield per test case executed.
 10. One or more non-transitorycomputer-readable storage media coupled to one or more processors andhaving instructions stored thereon which, when executed by the one ormore processors, cause the one or more processors to perform operationscomprising: receiving a log file that includes log records generatedfrom a code base; processing the log file through a pattern-miningalgorithm to determine at least one insight; generating a graphicalrepresentation based on an analysis of the at least one insight;processing the graphical representation through a machine-learningalgorithm to select a set of test cases from a plurality of test casesfor the code base and to assign a priority value to each of the selectedtest cases; sequencing the set of test cases based on the priorityvalues; and transmitting the sequenced set of test cases to a testexecution engine.
 11. The one or more non-transitory computer-readableof claim 10, wherein the operations further comprise: determining afailure pattern based on an analysis of testing results prior testing tothe code base, wherein the each of the test cases in the set of testcases is further selected based on the failure pattern, and wherein theassigned priority value for each of the test cases in the set of testcases is further determined based on the failure pattern.
 12. The one ormore non-transitory computer-readable of claim 10, wherein themachine-learning algorithm is a reward-seeking algorithm trained tomaximize defect yield per test case.
 13. The one or more non-transitorycomputer-readable of claim 10, wherein the operations further comprise:before receiving the log file, extracting terminologies from requirementdocuments; classifying the extracted terminologies into categories basedon a corpus of known terms; and generating process maps based on theclassified terminologies.
 14. The one or more non-transitorycomputer-readable of claim 10, wherein the operations further comprise:before selecting a set of test cases, clustering the plurality of testcases for the code base based on contextual similarity.
 15. A system,comprising: one or more processors; and a computer-readable storagedevice coupled to the one or more processors and having instructionsstored thereon which, when executed by the one or more processors, causethe one or more processors to perform operations comprising: receiving alog file that includes log records generated from a code base;processing the log file through a pattern-mining algorithm to determineat least one insight; generating a graphical representation based on ananalysis of the at least one insight; processing the graphicalrepresentation through a machine-learning algorithm to select a set oftest cases from a plurality of test cases for the code base and toassign a priority value to each of the selected test cases; sequencingthe set of test cases based on the priority values; and transmitting thesequenced set of test cases to a test execution engine.
 16. The systemof claim 15, wherein the operations further comprise: before receivingthe log file, receiving a test scenario and a context file selectedthrough a user interface, the context file including an object mapcomprising objects that correlate to respective components of a displaypage for the code base, the test scenario describing one of the testcases involving an intended interaction with at least one of thecomponents on the display page; correlating the intended interactionwith the at least one component with the corresponding object in theobject map; processing the intended interaction and the correspondingobject through an Artificial Intelligence (AI) model, the AI modeltrained using training data comprising a plurality of processes andrespective process steps supported by the components of the displaypage; determining a script template based on the processing and aselected automation tool; applying, based on the processing, the scripttemplate to the intended interaction and the correlated object togenerate an automated testing script for the selected automating tool;and assigning the generated automated testing script to the one of thetest cases.
 17. The system of claim 16, wherein each of the testscenarios is represented by a process graph that depicts a hierarchy ofbusiness process.
 18. The system of claim 15, wherein the operationsfurther comprise: before receiving the log file, determining acontextual distance for each of the test cases respective to each other;assigning each of the test cases to at least one cluster based on thecontextual distances; and assigning a score value to each test casesbased on a robustness to detect defects respective to the other testcases in the at least one cluster.
 19. The system of claim 18, whereinthe contextual distance for each test case includes a semantic distancefor each of the test cases respective to each other, a defect distancefor each of the test cases respective to each other, and a code hitdistance for each of the test cases respective to each other.
 20. Thesystem of claim 15, wherein the operations further comprise: determininga resolution for a defect, the defect reported based on a result of anexecution a testing script assigned to one of the sequence set of testcases; and implementing the resolution in the code base.